Bibliographic record
Abstract
Famines and the Making of Heritage is the first book to bring together groundbreaking research on the role of European famines in the nineteenth and twentieth centuries in relation to heritage making, museology, commemoration, education, and monument creation. Featuring contributions from famine experts across Europe and North America, the volume adopts a pioneering transnational perspective, and discusses issues such as contestable and repressed heritage, materiality, dark tourism, education on famines, oral history, multidirectional memory, and visceral empathy. Questioning why educational curricula and practices in schools and on heritage sites are region- or nation-oriented or transnational, chapters also consider whether they emphasise conflict or mutual understanding. Contributions also consider how present issues of European concern – such as globalisation, commodification, human rights, poverty, and migration – intersect with the heritage and memory of modern European famines. Lastly, the book considers what role emigrant and diasporic communities within and outside Europe play in the development of famine heritage and educational practices – and whether famine heritage is accessible to them. Famines and the Making of Heritage provides a crucial resource for museum and heritage scholars, students and professionals working on or with difficult or dark heritages, as well as those interested in the study of famines and legacies of troubled pasts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.667 | 0.502 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".